Field of the Invention
The present invention relates to a method and device for fusing a plurality of data, and, more particularly to a method and device for fusing a plurality of uncertain or correlated data to acquire a single optimal data.
Related Art
Data integration or fusion may refer to a technique to integrate or fuse a plurality of redundant data provided from a single data measurement sensor or a plurality of data measurement sensors respectively to acquire a single optimal and more certain data. For example, the plurality of redundant data may be measured by a single sensor sequentially or may be measured by a plurality of sensors respectively at the same time. This data fusion technique has been widely employed in a variety of fields, for example, including mobile robot engineering, estimation & tracking, remote sensing, automation, medical imaging, and wireless sensor networking fields. This data fusion technique may employ various different methods such as maximum likelihood, Kalman filter, fuzzy logic and covariance-based methods.
Specifically, the covariance-based method may be classified into a covariance union based method and a covariance intersection based method. The covariance-based method merges different covariance values using complicated combinations of weighted estimations. Therefore, in this method, a nonlinear cost function should be optimized, and repetitive operations are required, leading to a high operation complexity. Further, if two estimations with the same covariance matrix are taken into account, the above method may have a difficulty in use.
The conventional technique, for example, the Kalman filter may not be applied to a case in which there is correlation between the plurality of data, the technique. Otherwise, if applied, it will result in inconsistent estimate of the fused mean and covariance.